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It's a rare brand that has all its marketing channels managed under one roof. Far more typical is the model where some channels are managed in-house, and others are managed by one or more ad agencies. The result can be a measurement nightmare, made increasingly so by the number of channels and sources of performance data, as well as the infrastructure that may or may not be in place to normalize that data to a common set of apples-to-apples success metrics.

Back in 2011, I tested background image support in a variety of email clients. My testing revealed that background images can be easily added to the body of an email. However, at the table cell level, background images remained buggy in Outlook 2007 and 2010 — even with the use of Ros Hodgekiss' VML hack. Earlier this year, Hodgekiss announced a bulletproof solution for email background images, courtesy of Stig's lab. This solution harnesses VML to generate code for both email body and email table cell background images. Sound too good to be true? Well, that's what I set out to determine.

Setting The Stage

Like before, all tests were conducted using Litmus. Target email clients included the following:

• Android
• AOL Mail
• Apple Mail
• Gmail
• iPad Mail
• iPhone Mail
• Lotus Notes 8
• Outlook 2000
• Outlook 2002/XP
• Outlook 2003
• Outlook 2007
• Outlook 2010
• Outlook 2011
• Outlook 2013
• Yahoo! Mail

According to Stig's lab, the bulletproof background images should work in all of these, as well as Outlook.com (the web version) which is not offered by Litmus yet.

To generate Stig's bulletproof code, you simply paste in the URL to your background image, indicate a fallback color, and choose to apply the image to the full email body or single table cell. If you choose the single table cell, you must enter the dimensions of the table cell, or indicate that the dimensions are dynamic. The generated code will appear to the right, with instructions on where to insert it within your HTML. If you plan to copy over your own lines of HTML, be sure to transfer any inline styles to the new code.

Testing The Bulletproof Code On The Email Body

We already know that we can apply background images to the email body without VML, as long as we assign them to both the body tag and a wrapper table. Nevertheless, I wanted to see how the bulletproof code performed in this respect.

According to the Litmus previews, the bulletproof code works flawlessly in all major email clients.

On Table Cells

Back in 2010, I tested the VML hack to supposedly render background images on table cells in Outlook 2007 and Outlook 2010. Results were buggy, with both image and text behaving erratically. Needless to say, I was skeptical of the bulletproof solution for background images within table cells.

However, according to Litmus, the bulletproof code produced consistent background images on the table cell level. (The VML code from Stig's website has likely been updated from what was originally posted by Hodgekiss.)

Results

The chart below summarizes how the bulletproof code performed, compared to pure HTML.

table { margin-bottom: 30px; } td, th { padding: 15px; border: 1px solid #cccccc; text-align:center; } th { background-color: #383D40; color: #ffffff; border: 1px solid #383D40; text-align:left; } td.client { background-color: #f7f7f7; text-align:left; } div.yes-block { background: url(http://blog.mailermailer.com/wp-content/uploads/2013/06/green-check.png) no-repeat 50% 50%; width: 13px; height: 14px; line-height: 14px; margin: 0 auto; } div.no-block { background: url(http://blog.mailermailer.com/wp-content/uploads/2013/06/cross.png) no-repeat 50% 50%; width: 16px; height: 16px; line-height: 16px; margin: 0 auto; } th span.special { color: #92cbff; } Email Client Body Background Image
Pure HTML Body Background Image
Bulletproof Code Table Cell Background Image
Pure HTML Table Cell Background Image
Bulletproof Code Android Mail AOL Web Apple Mail Gmail iPad Mail iPhone Mail Lotus Notes 8+ Outlook 2000 Outlook 2002/XP Outlook 2003 Outlook 2007 Outlook 2010 Outlook 2011 Outlook 2013 Yahoo! Mail Conclusions

For background images on the email body, I would still recommend the pure HTML solution since it works just as well as the bulletproof code. (Why litter your code with VML if you don't have to?)

However, my previous solution requires one amendment to account for a peculiar bug I found. Essentially, a background color or fallback color must be assigned to the elements with background images (body and wrapper table). Otherwise, nested table cells that have a white background will actually render a transparent one within Gmail — bizarre I know.

Here's the amended code for pure HTML background images (substitute your own background image URL and desired fallback color).

<body background="http://x.com/image.jpg" style="background-color:#f6f6f6;"> <table background="http://x.com/image.jpg" style="background-color:#f6f6f6;" width="100%"> <tr> <td> (Your HTML Message Goes Here) </td> </tr> </table> </body>

Without the VML hack, background images on the table cell level are unsupported by several versions of Outlook – Outlook 2007, Outlook 2010, and Outlook 2013 (surprisingly Outlook 2011 will render background images on table cells). So, if you need to render a repeating background image on a table cell, the bulletproof code would be your best bet.

Other Background Image Properties Background-Repeat (No-Repeat)

Along with the background image support, I tested the background-repeat property for email body images (using pure HTML and the bulletproof code) and email table cell images (using the bulletproof code). For the pure HTML email body background image test, I simply added the "background-repeat:no-repeat" CSS style to both the body and wrapper table, like so:

<body background="http://x.com/image.jpg" style="background-repeat:no-repeat; background-color:#f6f6f6;"> <table background="http://x.com/image.jpg" style="background-repeat:no-repeat; background-color:#f6f6f6;" width="100%"> <tr> <td> (Your HTML Message Goes Here) </td> </tr> </table> </body>

For both of the bulletproof tests, I changed fill type from "tile" to "frame." See below (substitute your own background image and color):

<v:fill type="tile" src="http://i.imgur.com/n8Q6f.png" color="#f6f6f6"/> change to: <v:fill type="frame" src="http://i.imgur.com/n8Q6f.png" color="#f6f6f6"/>

Testing revealed that non-repeating background images are not supported for the email body, neither with pure HTML nor the bulletproof code. However, at the table cell level, the bulletproof code produced consistent, non-repeating background images.

Email Client Body Background No-Repeat
Pure HTML Body Background No-Repeat
Bulletproof Code Table Cell Background No-Repeat
Bulletproof Code Android Mail AOL Web Apple Mail Gmail iPad Mail iPhone Mail Lotus Notes 8+ Outlook 2000 Outlook 2002/XP Outlook 2003 Outlook 2007 Outlook 2010 Outlook 2011 Outlook 2013 Yahoo! Mail Background-Position

The VML background tags used by the bulletproof code are notoriously difficult to work with. To my knowledge, background positioning cannot be adjusted on them. Apparently, the VML shape and image tags are easier to work with, but still present challenges.

Given this setback, I couldn't test background positioning on the table cell level — the only scenario where the no-repeat property is well-supported.

Have you used the bulletproof code? How has it worked for you?

With so much discussion about marketing attribution recently, agreeing on a definition for the term can be a cumbersome process these days. And of course, within the attribution market there are also several different methodologies. Here are three things you should know about the differences between rules-based and data-driven attribution.

The average consumer may receive a dozen commercial emails per day— a number predicted to double by 2014. With so many messages coming in on a daily basis, it's easy for people to lose track of their inbox and overlook messages, even those from their favorite retailers. Email marketers understand the frustration of sorting through a pile of messages, which is why they rely on clever email techniques to boost re-engagement.

Check out these examples of how online retailers re-engage with their recipients. They just might inspire you to create your own compelling email re-engagement strategy.

Carter's

After a long delay since my last purchase, children’s clothing retailer, Carter’s reached out with this simple message:

They grabbed my attention with the subject line "Come Back, We Think You'll Love These" and kept it by presenting an enticing 20% discount off my next purchase.

Interestingly, the offer is below the fold; making the main purpose of this email to simply put Carter’s back on my radar. Overall, the design is clean, easy to follow, and the message is simple: don’t forget about us.

L.L. Bean

L.L. Bean took a slightly different approach to reunite with their readers.

Instead of a compelling offer, they used this re-engagement email to “catch up” with their inactive readers and:

• Talk about what the reader is missing out on
• Provide the opportunity to update email preferences
• Offer alternative ways to connect with L.L. Bean if email is no longer the best way

Piperlime

Piperlime used a playful tone in their re-engagement email to get dormant readers back on track.

Using the headline “Sometimes you just need space” and clever imagery to reinforce the message, Piperlime tries to motivate their readers and get them shopping again. They also give their readers the opportunity to opt out of the mailing list just as easily, but after a clever email like this filled with cliché remarks, who could resist?

Twitter

Twitter will often entice their readers back with a summary of Tweets that they’ve missed.

This type of re-engagement email from Twitter typically comes with an email subject line like, “MailerMailer, Presstacular, and 8 others have Tweets for you.” With this email Twitter attempts to alert (or remind) inactive Twitter users that the people or companies they follow and find interesting are sharing comments on Twitter.

True Citrus

Provider of high quality food products, True Citrus crafted a three-part re-engagement strategy in order to connect with their inactive readers.

With the subject line, "Why the cold shoulder?," True Citrus reached out to all of their subscribers who had not opened or clicked their email within the past 60 days. The subject line is playful, but gets right to the point and the content talks about what these subscribers are missing. The company even offers  a 25% off coupon code to motivate and re-engage with their readers.

The second part of their re-engagement email strategy was sent to the people who did not interact with their first email. At this point, True Citrus wants to know if these readers are in or out. The email is straight forward and gives recipients two options: keep the emails coming or opt-out.

The final stage of the True Citrus re-engagement strategy is a farewell email. This last email is sent to let people know their subscription has expired and they won't receive any additional emails from the company. However, they do cleverly slip in an exclusive offer at the end of this email, probably as a last ditch effort to retain customers.

Starbucks

Starbucks also attempts to re-engage with their readers by offering tempting offers including “Don’t Miss Your Free Birthday Reward” and “Buy Blonde Roast whole bean or VIA and get a $5 Starbucks Card.”

Such re-engagement emails help to lure their more passive customers to their website in order to update their account (and get their Birthday reward) or into the store to take advantage of Starbucks savings.

• Assigning Objects

We can create new data objects and variables quite easily within R. We use the = or the → operator to denote assigning an object to it’s name. For the purpose of this article we will use = to assign objectnames and objects. This is very useful when we are doing data manipulation as we can reuse the manipulated data as inputs for other steps in our analysis.

 

Types of Data Objects in R

• Lists

A list is simply a collection of data. We create a list using the c operator.

The following code creates a list named numlist from 6 input numeric data

numlist=c(1,2,3,4,5,78)

 

The following code creates a list named charlist from 6 input character data

charlist=c(“John”,”Peter”,”Simon”,”Paul”,”Francis”)

 

The following code creates a list named mixlistfrom both numeric and character data.

mixlist=c(1,2,3,4,”R language”,”Ajay”)

 

• Matrices

Matrix is a two dimensional collection of data in rows and columns, unlike a list which is basically one dimensional. We can create a matrix using the matrix command while specifying the number of rows by nrow and number of columns by ncol paramter.

In the following code , we create an matrix named ajay and the data is input in 3 rows as specified, but it is entered into first column, then second column , so on.

ajay=matrix(c(1,2,3,4,5,6,12,18,24),nrow=3)

ajay

[,1] [,2] [,3]

[1,] 1 4 12

[2,] 2 5 18

[3,] 3 6 24

 

However please note the effect of using the byrow=T (TRUE) option. In the following code we create an matrix named ajay and the data is input in 3 rows as specified, but it is entered into first row, then second row , so on.

 

>ajay=matrix(c(1,2,3,4,5,6,12,18,24),nrow=3,byrow=T)

>ajay

[,1] [,2] [,3]

[1,] 1 2 3

[2,] 4 5 6

[3,] 12 18 24

• Data Frames

A data frame is a list of variables of the same number of rows with unique row names. The column names are the names of the variables.

 

Tagged: Column, Dimension, Languages, Programming, rstats

This is a series of posts on Data Munging using R.

we will examine the various ways to input data and examine errors in the data input stage. We will accordingly study ways to detect errors and rectify them using the R language. People estimate that almost 60-70% of a project’s time goes in the data input, data quality and data validation stage. By the principle of Garbage-In -Garbage -Out, we believe that an analysis is only as good as the input quality of data. Thus data quality is both an integral part as well as one of the first stages in a project before we move to comprehensive statistical analysis.

Data Quality is an important part of studying data manipulation. How do we define Data Quality?

In this chapter, Data quality is defined as manipulating data in the desired shape, size and format. We further elaborate that as follows-

• Data that is useful for analysis without any errors is high quality data.

• Data that is problematic for accurate analysis because of any errors is low quality data.

• Data Quality errors are defined as deviations from actual data, due to systematic, computing or human mistakes.

• Rectifying data quality errors involves the steps of error detection, missing value imputation. It also involves using the feedback from these steps to design better data input mechanisms.

The major types of Data Quality errors are-

• Missing Data- This is defined as when data is simply missing. It may be represented by a “. “or a blank space or by special notation like NA (not available) . In R , missing data is represented by NA. Missing data is the easiest to detect but it is tough to rectify since most of the time we deal with data collected in real time in the past time and it is difficult and expensive to replace it with actual data. Some methods of replacing missing data is by imputing or inferring what the missing values could be , by looking at measures of central tendency like median , or mean, or by checking correlation with other variables or data points with better data population or by looking at historic data for a particular sub-set. Accordingly missing values for a particular data variable can be divided into sub sets for imputation by various means (like for different Geographic Values, or Different Time Values)

• Invalid Data (too high or too low numeric (and date-time) data, character data in invalid format).

• Incorrect Data (due to input errors including invalid or obsolete business rules, human input, low quality OCR scans)

The major causes of Data Quality Errors are-

• Human Error (due to input, typing )

• Machine Error ( due to invalid input readable eg. like by low resolution scanning device)

• Syntax Error ( due to invalid logic or assumptions)

• Data Format Error (due to a format that is not readable by software reading in data)

Steps for Diagnosis-

• Missing Value Detection (using functions related to is.NA) and Missing Value Imputation

• Distribution Analysis (using functions like summary,describe, and visualizations like boxplot, histogram)

• Outliers (Bonferroni) Detection and Outlier Capping ( Minimum- Maximum)

• Correlation with other variables ( using correlation statistics)

Diagnosis of Data Quality

 

The following functions in R will help us evaluate the quality of data in our data object.

• str- gives structure of object for a data frame including class, dimensions, variable names, variable types, first few observations of each variable)

• names- gives variable names.

• dim- dimensions of object.

• length- gives length of data object.

• nrow- gives number of rows of data object.

• ncol – gives number of columns of data object.

• class- gives data class of object. This can be list, matrix or data.frame or other classes.

We use the famous iris dataset and attach it or load it in our R session using the command

data(iris). We then try out each of the functions given above.

> data(iris)

> str(iris)

‘data.frame': 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...

$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...

$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

> names(iris)

[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"

> dim(iris)

[1] 150 5

> length(iris)

[1] 5

> nrow(iris)

[1] 150

> ncol(iris)

[1] 5

> class(iris)

[1] "data.frame"

It is quite clear that the str function by itself is enough for the first step data quality as it contains all the other parameters.

We now and try and print out a part of the object to check what is stored there. By default we can print the entire object by just writing it’s name. However this may be inconvenient in some cases when there are a large number of rows.

Accordingly we use the head and tail functions to look at the beginning and last rows in a data object.

• head – gives first few observations in a data object as specified by parameter in head (objectname, number of rows)

• tail -gives last few observations in a data object as specified by parameter in tail (objectname, number of rows)

Here we take the first 7 rows and the last 3 rows of dataset iris. Note that the first column in the output below is the row.number.
> head(iris,7)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species

1 5.1 3.5 1.4 0.2 setosa

2 4.9 3.0 1.4 0.2 setosa

3 4.7 3.2 1.3 0.2 setosa

4 4.6 3.1 1.5 0.2 setosa

5 5.0 3.6 1.4 0.2 setosa

6 5.4 3.9 1.7 0.4 setosa

7 4.6 3.4 1.4 0.3 setosa

> tail(iris,3)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species

148 6.5 3.0 5.2 2.0 virginica

149 6.2 3.4 5.4 2.3 virginica

150 5.9 3.0 5.1 1.8 virginica

We can also pass negative numbers as parameters to head and tail. Here we are trying to take the first and last 7 rows ( or numbers of rows in object -143 rows). Since the object iris has 150 rows , -143 evaluates to 7 in head and tail functions.

> head(iris,-143)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species

1 5.1 3.5 1.4 0.2 setosa

2 4.9 3.0 1.4 0.2 setosa

3 4.7 3.2 1.3 0.2 setosa

4 4.6 3.1 1.5 0.2 setosa

5 5.0 3.6 1.4 0.2 setosa

6 5.4 3.9 1.7 0.4 setosa

7 4.6 3.4 1.4 0.3 setosa

> tail(iris,-143)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species

144 6.8 3.2 5.9 2.3 virginica

145 6.7 3.3 5.7 2.5 virginica

146 6.7 3.0 5.2 2.3 virginica

147 6.3 2.5 5.0 1.9 virginica

148 6.5 3.0 5.2 2.0 virginica

149 6.2 3.4 5.4 2.3 virginica

150 5.9 3.0 5.1 1.8 virginica

1.2 Strings

One of the most common errors in data analytics is mismatch in string variables . String variables also known as character variables are non-numeric text, and even a single misplacement in white space, or upper case, lower case can cause discrepancies in the data. One of the most common types of data for which this error attains criticality is address data and name data.

From the perspective of R, the data “virginica” is a different data (or factor-level) from “ virginica” and from “Virginica”.“1600 Penn Avenue” is a different address from “1600 Pennsylvania Avenue” and from “1600 PA”. This can lead to escalation of costs especially since users of business analytics try and create unique and accurate contact details ( names and addresses). This attains even more importance for running credit checks and financial data, since an inaccurate data mismatch can lead to a wrong credit score to a person, leading to liability of the credit provider.

For changing case we use the functions toupper and tolower

> a=c("ajay","vijay","ravi","rahul","bharat")

> toupper(a)

[1] "AJAY" "VIJAY" "RAVI" "RAHUL" "BHARAT"

> b=c("Jane","JILL","AMY","NaNCY")

> tolower(b)

[1] "jane" "jill" "amy" "nancy"

sub,gsub,grepl

 

grepl can be used to find a part of a string . For example, in cricket we denote a not out score of 250 runs by a star, .i.e. 250* but denote a score of 250 out as 250. This can create a problem if we are trying to read in data. It will either treat it as character level data, or if we coerce it to return numeric values, it will show the not out scores by missing values.

We want to find all instance of “*” in address field and see if they are not out. grepl returns a logical vector (match or not for each element of x). We will further expand on this example in our Case Study for Cricket Analytics

table2$HSNotOut=grepl("\\*",table2$HS)

We use sub and gsub to substitute parts of string. While the sub function replaces the first occurrence, the gsub function replaces all occurrences of the matching pattern with the parameter supplied.

Here we are trying to replace white space in a sentence. Notice the sub function seems to work better than gsub in this case.

> newstring=" Hello World We are Experts in Learning R"

> sub(" ","",newstring)

[1] "Hello World We are Experts in Learning R"

> gsub(" ","",newstring)

[1] "HelloWorldWeareExpertsinLearningR"

Let us try to convert currency data into numeric data.For the sake of learning we are using a small data object , a list called “money” with three different inputs.

> money=c("$10,000","20000","32,000")

> money

[1] "$10,000" "20000" "32,000"

We replace a comma (used mainly for thousands in currency data) using gsub as shown before.

> money2=gsub(",","",money)

> money2

[1] "$10000" "20000" "32000"

$ indicates the end of a line in regular expressions. \$ is a dollar sign. So we have to use \\$ as an input in the gsub expression.

> money3=gsub("\\$","",money2)

> money3

[1] "10000" "20000" "32000"

At this point we may be satisfied that we have got the format we wanted. However this is an error, as these are still strings- as we find out by running the mean function

> mean(money3)

[1] NA

Warning message:

In mean.default(money3) : argument is not numeric or logical: returning NA

We then use the as operator to convert one data type (character) into another ( numeric).The as operator is generally used in syntax as.outputdataobject.class. Accordingly we will use as.numeric for the conversion.

 

> money4=as.numeric(money3)

> money4

[1] 10000 20000 32000

> mean(money4)

[1] 20666.67

Please note , we used many intermediate steps to do the multiple steps of data manipulation and used the = sign to assign this to new objects. We can combine two steps into one by putting them within successive brackets. This is illustrated below, when we are trying to convert character data containing (% Percentages) into Numeric data.

> mean(as.numeric(gsub("%","",percentages)))

[1] 35

> percentages

[1] "%20" "%30" "%40" "50"

Note we have found the mean but the original object is not changed.

 

Do gsub only one variable at a time

Slight problem is suppose there is data like 1,504 – it will be converted to NA instead of 1504.The way to solve this is use the nice gsub function ONLY on that variable. Since the comma is also the most commonly used delimiter , you dont want to replace all the commas, just only the one in that variable.

dataset$Variable2=as.numeric(paste(gsub(“,”,”",dataset$Variable)))

 

 

Additional- The function setAs creates methods for the as function to use. This is an advanced usage.

 

 

 

 

Tagged: Big Data, data, Data Integrity and Cleansing Tools, data quality, Data Warehousing, Database, Garbage in garbage out, Syntax Error

Background- The Russell–Einstein Manifesto was issued in London on July 9, 1955 by Bertrand Russell in the midst of the Cold War. It highlighted the dangers posed by nuclear weapons and called for world leaders to seek peaceful resolutions to international conflict. The signatories included eleven pre-eminent intellectuals and scientists, including Albert Einstein, who signed it just days before his death on April 18, 1955.

It is time Data Scientists most of whom have tacitly accepted that open source and open societies are movements for good, come together and stop enabling Big Brothers in China, India and United States from accumulating lifetimes of information on the citizens of the Internet. Does the 21st Century need a better equipped and enabled United Nations to deal with climate change, weather weapons and cyber warfare. While the myth of Anglo Saxon unipolar world is dead, renegade elements from the military industrial complex from both the West and East threaten the peace with illegal and unethical spying on citizens and civilians.

As data scientists, we enable them , these Governments with the new cyber nukes to collect, sort, aggregate, visualize , huge amounts of text data. I bet the NSA and China use Hadoop and R for purposes that are both repulsive and illegal. Now you can join the gravy train, and earn big money working for one, or the other, or even both.

Or we can come up with a newer version of Russell- Einstein manifesto for Big Data and Internet. The military industrial complex of the East and West has screwed the real planet enough. Why screw the cyber -world of Internet by these weapons of mass collection? Using Python, R, SAS, JMP, or using regression, k means, map reduce/.  Data scientists should have a conscience before they fire their code.

Big Brother is watching you- George Orwell

Big Data is watching you- George  Bush

Tagged: Albert Einstein, Bertrand Russell, Big Brothers Big Sisters of America, Big Data, Cold War, National Security Agency, Russell–Einstein Manifesto, united states

Tune in to the recent Beyond Web Analytics podcast on the topic of the coming Standards for digital data collection via tags. Listen as the BWA hosts Adam Greco, Rudi Shumpert explore the topic and coming standards with IBM’s Eliot Towb (product management for digital analytics and tag management), and Sri (Viswanath Srikanth) from IBM Standards.

Visit the W3C’s related standards group page to see the list of current members in the effort including the who is who in digital.

PlayJam will launch Disney game bundles, via its games network on smart TVs.

PlayJam will launch Disney game bundles, via its games network on smart TVs.

One of the more recent behavioral phenomena to emerge as a consistent pattern in a specific time span: mobile shopping.

The AOL On Network on Wednesday is expected to announce a partnership with online video platform Kaltura. The deal makes around 420,000 premium videos within the AOL On Network accessible from Kaltura's open-source video platform.

One of the more recent behavioral phenomena to emerge as a consistent pattern in a specific time span: mobile shopping.

The AOL On Network on Wednesday is expected to announce a partnership with online video platform Kaltura. The deal makes around 420,000 premium videos within the AOL On Network accessible from Kaltura's open-source video platform.

Bringing higher education to the masses also comes paradigm-shifting challenges. It has the potential of redefining the value of a "campus education" and to disrupt the traditional business model.

Bringing higher education to the masses also comes paradigm-shifting challenges. It has the potential of redefining the value of a "campus education" and to disrupt the traditional business model.

Spotify is launching its first in-house-created app to help users discover new music based on trending data like social buzz, playlists and what people are listening to and sharing. Chevy is on board as launch partner for the new Emerge app.

Spotify is launching its first in-house-created app to help users discover new music based on trending data like social buzz, playlists and what people are listening to and sharing. Chevy is on board as launch partner for the new Emerge app.

Mark Vranesh, chief accounting officer, was promoted to Zynga CFO. David Ko, previously Zynga's mobile chief, was named chief operating officer.

Mark Vranesh, chief accounting officer, was promoted to Zynga CFO. David Ko, previously Zynga's mobile chief, was named chief operating officer.